Econometric information recovery in behavioral networks

Artikel

In this paper, we suggest an approach to recovering behavior-related, preference-choice network information from observational data. We model the process as a self-organized behavior based random exponential network-graph system. To address the unknown nature of the sampling model in recovering behavior related network information, we use the Cressie-Read (CR) family of divergence measures and the corresponding information theoretic entropy basis, for estimation, inference, model evaluation, and prediction. Examples are included to clarify how entropy based information theoretic methods are directly applicable to recovering the behavioral network probabilities in this fundamentally underdetermined ill posed inverse recovery problem.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Journal: Econometrics ; ISSN: 2225-1146 ; Volume: 4 ; Year: 2016 ; Issue: 3 ; Pages: 1-11 ; Basel: MDPI

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Econometric and Statistical Methods and Methodology: General ; Single Equation Models; Single Variables: Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
• Thema: random exponential networks ; binary and weighed networks ; inverse problem ; adjacency matrix ; Cressie-Read family of divergence measures ; conditional moment conditions ; self organized behavior systems

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Judge, George
• Ereignis: Veröffentlichung
• (wer): MDPI
• (wo): Basel
• (wann): 2016

Weitere Informationen

• DOI: doi:10.3390/econometrics4030038
• Handle: http://hdl.handle.net/10419/171888
• Letzte Aktualisierung: 10.03.2025, 11:44 MEZ